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In the https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTrainingJob.html request, you specify the training algorithm. You can also specify algorithm-specific hyperparameters as string-to-string maps. The following table lists the hyperparameters for the Amazon SageMaker RCF algorithm. For more information, including recommendations on how to choose hyperparameters, see How RCF Works.
| Parameter Name | Description |
|---|---|
| feature_dim | The number of features in the data set. (If you are using the client libraries through a notebook, this value is calculated for you and need not be specified.) Required (When the job is run through the console.) Valid values: Positive integer (min: 1, max: 10000) |
| eval_metrics | A list of metrics used to score a labeled test data set. The following metrics can be selected for output: [See the AWS documentation website for more details] Optional Valid values: a list with possible values taken from accuracy or precision_recall_fscore. Default value: Both accuracy, precision_recall_fscore are calculated. |
| num_samples_per_tree | Number of random samples given to each tree from the training data set. Optional Valid values: Positive integer (min: 1, max: 2048) Default value: 256 |
| num_trees | Number of trees in the forest. Optional Valid values: Positive integer (min: 50, max: 1000) Default value: 100 |